In collaboration with Shuo Gu, we have pursued a comprehensive examination of transcriptome-wide RNA-RNA interactions (RNA duplexes longer than 18 nucleotides) across 4 different cell lines using 3 different treatments (native cell lysates, removal of proteins, and removal of ribosomal RNA and proteins). MySeq reads were examined after treating with endoribonucleases targeting single stranded RNAs. These reads were correlated with a variety of computational bioinformatic analyses of the potential interactions. The prevalence or lack thereof was determined to enable a better understanding of how cellular RNA interacts with its cellular environment. Interestingly the major finding was that there are very few RNA-RNA interactions found across the 4 cell lines, thus indicating that such interactions are avoided. The number of interactions went up significantly when proteins were removed from the lysates and the sequences re-annealed. The majority of duplexes involved ribosomal transcripts. The diversity of results from the different cell lines suggests that RNA-RNA interactions are to a large extent stochastic in nature. Cells presumably avoid such interactions to prevent activation of innate immune responses, which are normally reserved for viruses. ---The functionality of Drosha in cellular systems is important for understanding the processing of microRNAs and how they relate to normal cellular activity as well as diseases such as cancer. In another collaboration with Shuo Gu the relationship of Drosha targeted stem-loop structures and the type of microRNA isforms that are produced was examined. Experimental and computational approaches were applied to determine these relationships. Results indicate that bent, distorted and/or flexible structures in the targeted Drosha stem seem to facilitate the production of alternate forms of microRNA. Structural predictions and experimental results were compared and correlated. Specifically, cleavage of pri-miR-9-1, but not pri-miR-9-2 or pri-miR-9-3, generates an alternative miR-9 with a shifted seed sequence that exapands the scope of its target RNAs. Interestingly, analysis of low-grade glioma patient samples indicate that alternative pri-miR-9 has a potential rolein tumor progression. ---A collaboration with Esta Sterneck's laboratory is ongoing. Her lab investigates cell signaling pathways involved in breast and glioblastoma tumorigenesis with a focus on the transcription factor CCAAT/enhancer binding protein delta (CEBPD) using in vitro cell culture and in vivo mouse model systems. Using a transgenic mouse model of breast cancer, her group has shown that CEBPD exhibits a dual role in mammary tumorigenesis. On the one hand, CEBPD prevents tumor multiplicity and on the other hand, CEBPD promotes distant lung metastases. In addition, CEBPD promotes stem-like cancer cells, which have been implicated in tumor metastasis and treatment resistance, in breast and glioblastoma tumor cells through regulation of various signaling pathways and stemness. In addition, strategies for targeting the message of CEBPD are necessary to down regulate CEBPD-mediated tumor progression signaling. As a tie-in to our nanobiology project, our laboratory is developing approaches for RNAi therapeutics to knock down the CEBPD mRNA by delivering strategically designed RNA nanostructures as their own entities or in combination with lipid carriers.Initial results with our lipid-based carriers look promising and we are currently progressing to the use of mouse models for further verification. --- In cell SHAPE prediction provides a new level of detail for determining RNA structure within cells. These results may vary from the more standard SHAPE techniques that do not take the cellular environment into account when producing potential structural predictions. We are developing a method for the computational prediction of in cell SHAPE by training a neural network (which has be optimized by hyper-parmaterization techniques) based on known in cell SHAPE measurements obtained from an E. coli database. Predictions, given a sequence, seem to be producing reasonably accurate results. This is the first time that computational methods have been applied to the prediction of RNA structure within cells based on machine learning. ---The prediction of RNA secondary and 3D structures containing non-canonical base pair interactions is a difficult and important problem that needs better algorithms. We are developing a set of computational algorithms based on Bayesian and neural network methodologies to enable the prediction of canonical and more importantly non-canonical base pair interactions in RNA. A large database has been compiled containing a multitude of structures including the non-canonical base pair interactions. The algorithms have shown significant utility, enabling the prediction of complex motifs at the secondary structure level. These results are then being used in conjunction with an RNA 3D structure generation program, which enables the prediction of 3D RNA structures that incorporate the complex non-canonical interactions. This set of algorithms are also being applied to the prediction of multi-sequence RNA nano-assemblies. ---In another project in collaboration with Mikhail Kashlev we are developing a methodology using a neural network approach(machine learning) to determine transcriptional pause sites in bacterial cells. Sequence reads are being produced by a method called RNet-seq (a variation of Net-seq) to define pause sites. The neural net being developed using these reads discriminates between these types of sites in various bacterial sequences (E. coli and B. subtilis). The approach seems to indicate that their are significant differences between these bacterial systems in regards go pausing. Sequence and structural variations around the pause site play a significant role in addition to co-factors. All this information in being incorporated into the machine learning approaches to aid in the discrimination process.---Another project in collaboration with Stuart Le Grice involves the development of a computational approach to determined binding sites and affinities of small molecules targeting various RNA structural motifs. The goal of this project is to aid in the screening of small molecules for their potential to be therapeutically beneficial in targeting viral RNAs or cancer causing genes. The small molecules are initially derived from sets found by binding to experimental screening methods using small molecule microarrays. The pipeline as it currently stands is able to determine to a reasonable level of accuracy ligand poses as well as the conformation of the binding pockets. It also seem able to discriminate between different levels of binding affinities for different ligands. ---An algorithm, RiboSketch, has been developed for the depiction of nucleic acid secondary structures which may contain multiple strands consisting of RNA or DNA. Layout algorithms, comprehensive editing capabilities, and a multitude of simulation modes are available within the system. Interactive features allow RiboSketch to create publication quality diagrams for structures with a wide range of composition, size, and complexity. The program may be run in any web browser without the need for installation, or as a standalone Java application.